scholarly journals Beyond building damage: estimating and understanding non-recovery following disasters

2021 ◽  
Author(s):  
Sabine Loos ◽  
David Lallemant ◽  
Feroz Khan ◽  
Jamie McCaughey ◽  
Robert Banick ◽  
...  
Keyword(s):  
Population Density ◽  
Secondary Data ◽  
Building Damage ◽  
Nepal Earthquake ◽  
Wide Range ◽  
Damage Data ◽  
Recovery Resources ◽  
Post Disaster ◽  
2015 Nepal Earthquake

Abstract Following a disaster, crucial decisions about recovery resources often focus on immediate impact, partly due to a lack of detailed information on who will struggle to recover. Here we perform an analysis of surveyed data on reconstruction and secondary data commonly available after a disaster to estimate a metric of non-recovery or the probability that a household could not fully reconstruct within five years after an earthquake. Analyzing data from the 2015 Nepal earthquake, we find that non-recovery is associated with a wide range of factors beyond building damage, such as ongoing risks, population density, and remoteness. If such information were available after the 2015 earthquake, it would have highlighted that many damaged areas have differential abilities to reconstruct due to these factors. More generally, moving beyond damage data to evaluate and quantify non-recovery will support effective post-disaster decisions that consider pre-existing differences in the ability to recover.

scholarly journals Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images

2020 ◽  
Vol 12 (12) ◽  
pp. 1924 ◽  
Author(s):  
Hiroyuki Miura ◽  
Tomohiro Aridome ◽  
Masashi Matsuoka
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Building Damage ◽  
Aerial Images ◽  
Automated Identification ◽  
Damage Data ◽  
Collapsed Buildings ◽  
Post Disaster ◽  
Earthquake Data

A methodology for the automated identification of building damage from post-disaster aerial images was developed based on convolutional neural network (CNN) and building damage inventories. The aerial images and the building damage data obtained in the 2016 Kumamoto, and the 1995 Kobe, Japan earthquakes were analyzed. Since the roofs of many moderately damaged houses are covered with blue tarps immediately after disasters, not only collapsed and non-collapsed buildings but also the buildings covered with blue tarps were identified by the proposed method. The CNN architecture developed in this study correctly classifies the building damage with the accuracy of approximately 95 % in both earthquake data. We applied the developed CNN model to aerial images in Chiba, Japan, damaged by the typhoon in September 2019. The result shows that more than 90 % of the building damage are correctly classified by the CNN model.

Post-disaster Waste Management: Lessons Learnt from 2015 Nepal Earthquake

2019 ◽  
pp. 465-483 ◽  
Author(s):  
Nivesh Dugar ◽  
Sailesh Karanjit ◽  
Nawa Raj Khatiwada ◽  
Surya Man Shakya ◽  
Anish Ghimire
Keyword(s):  
Waste Management ◽  
Nepal Earthquake ◽  
Lessons Learnt ◽  
Post Disaster ◽  
2015 Nepal Earthquake

scholarly journals BDD-Net: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1670 ◽  
Author(s):  
Jinyuan Shao ◽  
Lina Tang ◽  
Ming Liu ◽  
Guofan Shao ◽  
Lang Sun ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Semantic Information ◽  
Building Damage ◽  
Pixel Classification ◽  
Single Model ◽  
Field Surveys ◽  
Wide Range ◽  
Disaster Assessment ◽  
Post Disaster

The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not always feasible. The use of satellite imagery for disaster assessment can overcome this problem. However, the textural and contextual features of post-event satellite images vary with disaster types, which makes it difficult to use models that have been developed for a specific disaster type to detect damaged buildings following other types of disasters. Therefore, it is hard to use a single model to effectively and automatically recognize post-disaster building damage for a broad range of disaster types. Therefore, in this paper, we introduce a building damage detection network (BDD-Net) composed of a novel end-to-end remote sensing pixel-classification deep convolutional neural network. BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. Pre- and post-disaster images were provided as input for the network to increase semantic information, and a hybrid loss function that combines dice loss and focal loss was used to optimize the network. Publicly available data were utilized to train and test the model, which makes the presented method readily repeatable and comparable. The protocol was tested on images for five disaster types, namely flood, earthquake, volcanic eruption, hurricane, and wildfire. The results show that the proposed method is consistently effective for recognizing buildings damaged by different disasters and in different areas.

scholarly journals The 2015 Nepal Earthquake Disaster: Is the Threat of Occurrence of Communicable Disease Epidemic Over?

2018 ◽  
Vol 1 (1) ◽  
pp. 105-110 ◽  
Author(s):  
Sakiko Kanbara ◽  
Nlandu Roger Ngatu (Corresponding author) ◽  
Tara Pokhrel T ◽  
Apsara Pandey ◽  
Chandrakara Sharma ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Communicable Disease ◽  
Health Assessment ◽  
Health Events ◽  
Governmental Agencies ◽  
Nepal Earthquake ◽  
Assessment And Monitoring ◽  
Monitoring And Surveillance ◽  
Post Disaster ◽  
2015 Nepal Earthquake

This opinion paper highlights the state of public health assessment in evacuation centers following the 2015 Nepal earthquake. It also suggests an approach to reinforce risk assessment and surveillance of communicable diseases (CD) in remote Nepalese districts. A short surveillance research was conducted on outbreaks of infectious diseases in Nepal in the post-2015 earthquake in evacuation centers in Kathmandu and Dhading districts. In collaboration with the Nursing Association of Nepal (NAN), the researchers have established a monitoring and surveillance system, named ‘EpiNurse’ program, in remote Nepalese districts. Periodic shelter to shelter visits, CD risk assessment and relief needs inventory in local communities are implemented, whereas health events with a potential to cause a CD outbreak are being reported to governmental agencies and health clusters involved in post-disaster relief in Nepal. Several cases of diarrheal diseases were identi fied in Nepalese districts after the 2015 earthquake, suggesting the existence of potential risk for the occurrence of new CD epidemics. Onsite risk assessment and monitoring of the effectiveness of actions and interventions implemented, as well as improvement of risk communication between relief agencies should be expanded to less resourced districts to reduce the risk of CD outbreak occurrence.

scholarly journals Earthquakes Reconnaissance Data Sources, a Literature Review

2021 ◽  
Author(s):  
Diana Maria Contreras Mojica ◽  
Sean Wilkinson ◽  
Philip James
Keyword(s):  
Laser Scanning ◽  
Building Damage ◽  
Current Data ◽  
Data Sources ◽  
Damage Data ◽  
Efficient Data ◽  
Data Source ◽  
Single Data ◽  
Post Disaster ◽  
Selection Of

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and guide more efficient data. This paper reviews 38 articles that indicate the sources used by different authors to collect data related to damages and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria. Nowadays, reconnaissance mission can not rely on a single data source, and different data sources should complement each other, validate collected data, or quantify the damage comprehensively. The recent increase in the number of crowdsourcing and SM platforms as a source of data for earthquake reconnaissance is a clear indication of the tendency of data sources in the future.

Post-disaster mental health and psychosocial support: Experience from the 2015 Nepal earthquake

2017 ◽  
Vol 6 (1) ◽  
pp. 22 ◽  
Author(s):  
Kedar Marahatta ◽  
Surendra Sherchan ◽  
Reuben Samuel ◽  
Nazneen Anwar ◽  
MarkHumphrey Van Ommeren ◽  
...  
Keyword(s):  
Mental Health ◽  
Psychosocial Support ◽  
Disaster Mental Health ◽  
Nepal Earthquake ◽  
Post Disaster ◽  
2015 Nepal Earthquake

Impact of Nepal Earthquake on Patients Presenting for Emergency Care at Patan Hospital

2018 ◽  
Vol 13 (02) ◽  
pp. 211-216 ◽  
Author(s):  
Mimang Tembe ◽  
Sushma Dhakal ◽  
Ashis Shrestha ◽  
Josh Mugele ◽  
Darlene R. House
Keyword(s):  
Emergency Department ◽  
Disaster Preparedness ◽  
Health Sector ◽  
Emergency Department Utilization ◽  
Patient Volume ◽  
Nepal Earthquake ◽  
Clinical Conditions ◽  
Post Disaster ◽  
2015 Nepal Earthquake ◽  
Insight Into

AbstractObjectiveNatural disasters have a significant impact on the health sector. On April 25, 2015, Nepal was struck by a 7.8 magnitude earthquake. The aim of the study was to compare patient volumes and clinical conditions presenting to the emergency department pre- and post-earthquake.MethodsA retrospective study was done at Patan Hospital Emergency Department in Kathmandu, Nepal. Volume, demographics, and patient diagnoses were collected for 4 months post-disaster and compared with cases seen the same months the year before the disaster to control for seasonal variations.ResultsAfter the 2015 Nepal earthquake, 12,180 patients were seen in the emergency department. This was a significant decrease in patient volume compared with the 14,971 patients seen during the same months in 2014 (P=0.04). Of those, 5496 patients (4093 pre-disaster and 1433 post-disaster) had a chief complaint or diagnosis recorded for analysis. An increase in cardiovascular and respiratory cases was seen as well as an increase in psychiatric cases (mostly alcohol related) and cases of anemia. There was a decrease in the number of obstetrics/gynecology, infectious disease, and poisoning cases post-earthquake.ConclusionsUnderstanding emergency department utilization after the earthquake has the potential to give further insight into improving disaster preparedness plans for post-disaster health needs. (Disaster Med Public Health Preparedness. 2019;13:211–216).

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

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